ML with HE: Privacy Preserving Machine Learning Inferences for Genome Studies
This work addresses privacy concerns for genome studies in cloud computing, but it is incremental as it applies existing encryption and ML methods to a specific domain.
The paper tackles the problem of preserving privacy in cloud-based genome analysis by proposing a secure multi-label tumor classification method using homomorphic encryption, achieving classification on encrypted genome data with SVM and XGBoost algorithms.
Preserving the privacy and security of big data in the context of cloud computing, while maintaining a certain level of efficiency of its processing remains to be a subject, open for improvement. One of the most popular applications epitomizing said concerns is found to be useful in genome analysis. This work proposes a secure multi-label tumor classification method using homomorphic encryption, whereby two different machine learning algorithms, SVM and XGBoost, are used to classify the encrypted genome data of different tumor types.